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The platform is found to be cost-effective and is envisaged to be a significant contribution to intelligent transportation systems for road traffic monitoring. The availability of physical locations enables a myriad of applications, as exemplified extensively throughout this book. A particular application domain that benefits from the availability of location information is sensor network routing. Specifically the prospects brought by recent developments in WSN localization have sparked interest on a category of routing algorithms, known as geographical routing (D.

Denote the a priori known distribution of the non-anchor nodes by g(X). , g(X) is a constant function. The above estimators have often been used to obtain a point estimate of the non-anchors’ locations. In some applications, we are interested in knowing in which region a non-anchor node is located. Such knowledge is often useful in asset management for example. Both the ML estimator and the MAP estimator can be altered to generate such location information. Assume that the entire network area is 21 Introduction to Wireless Sensor Network Localization divided into M regions and each region is labelled by Lk ,1 ≤ k ≤ M .

The localization problem is formulated as a multi-hypothesis testing problem and the authors provide an asymptotic performance guarantee of the system. The authors further investigate the optimal placement of anchor nodes in the system. The optimal placement problem is formulated as a mixed integer linear programming problem and a fast algorithm is presented for solving the problem. Finally the proposed techniques are validated using testbed implementations involving MICAz motes manufactured by Crossbow.